pacman::p_load(tmap, sf, DT, performance, ggpubr, stplanr, tidyverse)Hands-on 3 - Processing and Visualising Flow DataR
15.1 Overview
Spatial interaction represent the flow of people, material, or information between locations in geographical space. It encompasses everything from freight shipments, energy flows, and the global trade in rare antiquities, to flight schedules, rush hour woes, and pedestrian foot traffic.
Each spatial interaction, as an analogy for a set of movements, is composed of a discrete origin/destination pair. Each pair can be represented as a cell in a matrix where rows are related to the locations (centroids) of origin, while columns are related to locations (centroids) of destination. Such a matrix is commonly known as an origin/destination matrix, or a spatial interaction matrix.
In this hands-on exercise, you will learn how to build an OD matrix by using Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall. By the end of this hands-on exercise, you will be able:
to import and extract OD data for a selected time interval,
to import and save geospatial data (i.e. bus stops and mpsz) into sf tibble data frame objects,
to populate planning subzone code into bus stops sf tibble data frame,
to construct desire lines geospatial data from the OD data, and
to visualise passenger volume by origin and destination bus stops by using the desire lines data.
15.2 Getting Started
For the purpose of this exercise, four r packages will be used. They are:
sf for importing, integrating, processing and transforming geospatial data. tidyverse for importing, integrating, wrangling and visualising data. tmap for creating thematic maps.
15.3
Preparing the Flow Data 15.3.1 Importing the OD data Firstly, we will import the Passenger Volume by Origin Destination Bus Stops data set downloaded from LTA DataMall by using read_csv() of readr package.
odbus <- read_csv("data/aspatial/origin_destination_bus_202310.csv")Let use display the odbus tibble data table by using the code chunk below.
glimpse(odbus)Rows: 5,694,297
Columns: 7
$ YEAR_MONTH <chr> "2023-10", "2023-10", "2023-10", "2023-10", "2023-…
$ DAY_TYPE <chr> "WEEKENDS/HOLIDAY", "WEEKDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 16, 16, 14, 14, 17, 17, 17, 7, 14, 14, 10, 20, 20,…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <chr> "04168", "04168", "80119", "80119", "44069", "2028…
$ DESTINATION_PT_CODE <chr> "10051", "10051", "90079", "90079", "17229", "2014…
$ TOTAL_TRIPS <dbl> 3, 5, 3, 5, 4, 1, 24, 2, 1, 7, 3, 2, 5, 1, 1, 1, 1…odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE)15.3.2 Extracting the study data
For the purpose of this exercise, we will extract commuting flows on weekday and between 6 and 9 o’clock.
odbus6_9 <- odbus %>%
filter(DAY_TYPE == "WEEKDAY") %>%
filter(TIME_PER_HOUR >= 6 & TIME_PER_HOUR <= 9) %>%
group_by(ORIGIN_PT_CODE, DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))
write_rds(odbus6_9,"data/rds/odbus6_9.rds")
odbus6_9 <- read_rds("data/rds/odbus6_9.rds")Table below shows the content of odbus6_9
datatable(odbus6_9)15.4 Working with Geospatial Data
For the purpose of this exercise, two geospatial data will be used. They are:
BusStop: This data provides the location of bus stop as at last quarter of 2022.
MPSZ-2019: This data provides the sub-zone boundary of URA Master Plan 2019. Both data sets are in ESRI shapefile format.
15.4.1 Importing geospatial data
Two geospatial data will be used in this exercise, they are:
busstop <- st_read(dsn = "data/geospatial", layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`C:\weipengten\ISSS624\Hands-on_Ex\Hands-on_Ex3\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5159 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21mpsz <- st_read(dsn = "data/geospatial", layer = "MPSZ-2019") %>%
st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`C:\weipengten\ISSS624\Hands-on_Ex\Hands-on_Ex3\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84Note st_read() function of sf package is used to import the shapefile into R as sf data frame. st_transform() function of sf package is used to transform the projection to crs 3414.
15.5 Geospatial data wrangling
15.5.1 Combining Busstop and mpsz
Code chunk below populates the planning subzone code (i.e. SUBZONE_C) of mpsz sf data frame into busstop sf data frame.
busstop_mpsz <- st_intersection(busstop, mpsz) %>%
select(BUS_STOP_N, SUBZONE_C) %>%
st_drop_geometry()
write_rds(busstop_mpsz, "data/rds/busstop_mpsz.rds")Next, we are going to append the planning subzone code from busstop_mpsz data frame onto odbus6_9 data frame.
od_data <- left_join(odbus6_9 , busstop_mpsz, by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
rename(ORIGIN_BS = ORIGIN_PT_CODE, ORIGIN_SZ = SUBZONE_C, DESTIN_BS = DESTINATION_PT_CODE)Before continue, it is a good practice for us to check for duplicating records.
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
duplicate# A tibble: 1,022 × 4
ORIGIN_BS DESTIN_BS TRIPS ORIGIN_SZ
<chr> <fct> <dbl> <chr>
1 22501 22009 1 JWSZ09
2 22501 22009 1 JWSZ09
3 22501 22451 167 JWSZ09
4 22501 22451 167 JWSZ09
5 22501 22469 28 JWSZ09
6 22501 22469 28 JWSZ09
7 22501 22479 20 JWSZ09
8 22501 22479 20 JWSZ09
9 22501 22509 4 JWSZ09
10 22501 22509 4 JWSZ09
# ℹ 1,012 more rowsIf duplicated records are found, the code chunk below will be used to retain the unique records.
od_data <- unique(od_data)It will be a good practice to confirm if the duplicating records issue has been addressed fully.
Next, we will update od_data data frame cwith the planning subzone codes.
od_data <- left_join(od_data , busstop_mpsz, by = c("DESTIN_BS" = "BUS_STOP_N"))
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()
duplicate# A tibble: 1,324 × 5
ORIGIN_BS DESTIN_BS TRIPS ORIGIN_SZ SUBZONE_C
<chr> <chr> <dbl> <chr> <chr>
1 01013 51071 2 RCSZ10 CCSZ01
2 01013 51071 2 RCSZ10 CCSZ01
3 01112 51071 66 RCSZ10 CCSZ01
4 01112 51071 66 RCSZ10 CCSZ01
5 01112 53041 4 RCSZ10 BSSZ01
6 01112 53041 4 RCSZ10 BSSZ01
7 01121 51071 8 RCSZ04 CCSZ01
8 01121 51071 8 RCSZ04 CCSZ01
9 01121 82221 1 RCSZ04 GLSZ05
10 01121 82221 1 RCSZ04 GLSZ05
# ℹ 1,314 more rowsod_data <- unique(od_data)
od_data <- od_data %>%
rename(DESTIN_SZ = SUBZONE_C) %>%
drop_na() %>%
group_by(ORIGIN_SZ, DESTIN_SZ) %>%
summarise(MORNING_PEAK = sum(TRIPS))
write_rds(od_data, "data/rds/od_data.rds")
od_data <- read_rds( "data/rds/od_data.rds")15.6 Visualising Spatial Interaction
In this section, you will learn how to prepare a desire line by using stplanr package.
15.6.1 Removing intra-zonal flows
We will not plot the intra-zonal flows. The code chunk below will be used to remove intra-zonal flows.
od_data1 <- od_data[od_data$ORIGIN_SZ!=od_data$DESTIN_SZ,]15.6.2 Creating desire lines
In this code chunk below, od2line() of stplanr package is used to create the desire lines.
flowLine <- od2line(flow = od_data1, zones = mpsz, zone_code = "SUBZONE_C")15.6.3 Visualising the desire lines
To visualise the resulting desire lines, the code chunk below is used.
tm_shape(mpsz) + tm_polygons() + flowLine %>%
tm_shape() + tm_lines(lwd = "MORNING_PEAK", style = "quantile", scale = c(0.1, 1, 3, 5, 7, 10), n = 6, alpha = 0.3)
When the flow data are very messy and highly skewed like the one shown above, it is wiser to focus on selected flows, for example flow greater than or equal to 5000 as shown below.
tm_shape(mpsz) + tm_polygons() + flowLine %>%
filter(MORNING_PEAK >= 5000) %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3) 
```